The positioning of a moving platform, such as, vehicles, vessels, or individuals, is commonly achieved using known reference-based systems, such as the Global Navigation Satellite Systems (GNSS). The GNSS comprises a group of satellites that transmit encoded signals to receivers on the ground that, by means of trilateration techniques, can calculate their position using the travel time of the satellites' signals and information about the satellites' current location. Such positioning techniques are also commonly utilized to position a device (such as for example, among others, a mobile phone) within or on the moving platform, whether such device is tethered or non-tethered to the moving platform. Currently, the most popular form of GNSS for obtaining absolute position measurements is the global positioning system (GPS), which is capable of providing accurate position and velocity information provided that there is sufficient satellite coverage. However, where the satellite signal becomes disrupted or blocked such as, for example, in urban settings, tunnels and other GNSS-degraded or GNSS-denied environments, a degradation or interruption (i.e. “gap”) in the GPS positioning information can result. As a work around, Assisted Global Positioning System (AGPS) receiver chipsets (in addition to significantly improving the startup performance by utilizing network connection) also further use high sensitivity capabilities to provide absolute positions of the platform even in some environments that cannot guarantee clear line of sight to satellite signals. This results in more availability, however, the quality can be poor for such measurements.
In order to achieve more accurate, consistent and uninterrupted positioning information, GNSS information may be augmented with additional positioning information obtained from complementary positioning systems. Such systems may be self-contained and/or non-reference based systems within the device or the platform, and thus need not depend upon external sources of information that can become interrupted or blocked.
One such non-reference based or relative positioning system is the inertial navigation system (INS). Inertial sensors are self-contained sensors within the device or platform that use gyroscopes to measure rate of rotation/angle, and accelerometers to measure specific force (from which acceleration is obtained). Using initial estimates of position, velocity and orientation angles of the device or platform as a starting point, the INS readings can subsequently be integrated over time and used to determine the current position, velocity and orientation angles of the device and its relative misalignment within the platform. Typically, measurements are integrated once for gyroscopes to yield orientation angles and twice for accelerometers to yield position of the device or platform incorporating the orientation angles. Thus, the measurements of gyroscopes will undergo a triple integration operation during the process of yielding position. Inertial sensors alone, however, are unsuitable for accurate positioning because the required integration operations of data results in positioning solutions that drift with time, thereby leading to an unbounded accumulation of errors.
Further problems in providing accurate position or navigation information about a mobile device can arise where the device is capable of moving freely (e.g. without any constraints) or can move with some constraints within the moving platform. Inaccuracies can arise in such cases because the coordinate frame of the inertial sensors (accelerometers and gyroscopes) of the device is not aligned with the coordinate frame of the moving platform. The device and the moving platform can be misaligned with respect to one another, and such misalignment can change over time. For example, where the device moves freely without constraint, the misalignment of the device and the platform can change without constraint. Where the device is capable of constrained movement, the misalignment of the device and the platform can also change, wherein the change is subject to constraints. Where the mobile device is mounted within the platform, there may still be a misalignment where such mounting results in a misalignment between the coordinate frame of the device and the coordinate frame of the platform (although such misalignment would not change over time). It should be noted that a skilled person would know and understand that the misalignment between a mobile device and a moving platform is different than the misalignment that might occur where a navigation module for positioning a moving platform is positioned incorrectly within the moving platform, thereby resulting in a misalignment between the module and the moving platform.
Given that the positioning techniques described above may suffer loss of information or errors in data, common practice involves integrating the information/data obtained from the GNSS with that of the complementary system(s). For instance, to achieve a better positioning solution, INS and GPS data may be integrated because they have complementary characteristics. INS readings are accurate in the short-term, but their errors increase without bounds in the long-term due to inherent sensor errors. GNSS readings are not as accurate as INS in the short-term, but GNSS accuracy does not decrease with time, thereby providing long-term accuracy. Also, GNSS may suffer from outages due to signal blockage, multipath effects, interference or jamming, while INS is immune to these effects.
Although available, integrated INS/GNSS is not often used commercially for low cost applications because of the relatively high cost of navigational or tactical grades of inertial measurement units (IMUs) needed to obtain reliable independent positioning and navigation during GNSS outages. Low cost, small, lightweight and low power consumption Micro-Electro-Mechanical Systems (MEMS)-based inertial sensors may be used together with low cost GNSS receivers, but the performance of the navigation system will degrade quickly in contrast to the higher grade IMUs in areas with little or no GNSS signal availability due to time-dependent accumulation of errors from the INS.
Speed information from the odometric readings when in vehicle together with other corresponding motion constraints, or pedestrian dead-reckoning in case of walking together with other corresponding motion constraints, may be used to enhance the performance of the MEMS-based integrated INS/GNSS solution or replace the full-INS, however, current such systems continue to be plagued with the growth of errors over time during GNSS outages.
It is important to provide absolute updates to the navigation system that is incorporating inertial sensors especially when GNSS is not present or during long GNSS outages such as indoors or in parkades. Although not dedicated for positioning and navigation, several wireless communication systems are now widely used such as for example wireless local area network (WLAN) commonly referred to as “WiFi”, which is heavily deployed in indoor environments. These are signals of opportunity and can be used in positioning. Thus, wireless positioning requires getting information from wireless transceivers at different user locations. Different techniques for wireless positioning might be used, with different accuracies, such as for example, time of arrival, time difference of arrival, angles of arrival, received signal strength, and fingerprinting techniques, among others. Some of the common techniques used for wireless positioning with better accuracies are through wireless information being mapped in databases by deploying pre-surveys of the indoor environments which is then used to estimate the user positions. This is a drawback for these techniques. Some other techniques do not need pre-existing information, but they suffer from decreased accuracy.
Hence, there is a need to provide enhanced positioning performance from an integrated navigation system that utilizes wireless positioning with other sensors and systems, where the wireless positioning does not need pre-existing information such as pre-surveys while still providing improved accuracy.